Generalized Blockmodeling

Doreian, Patrick; Batagelj, Vladimir; Ferligoj, Anuska

doi:10.1017/cbo9780511584176

Cited by 297 publications

(405 citation statements)

References 0 publications

Supporting

Mentioning

389

Contrasting

Unclassified

Order By: Relevance

“…A characteristic feature of such networks is that distinct, yet species-wise similar pollinator species tend to visit the similar plant species and vice versa. In network theory, nodes that are adjacent to the same set of nodes are called structurally equivalent [28]. It is known that statistical inference methods can be successfully applied to networks to aggregate nodes according to their degree of structural equivalence [29].…”

Section: Discussionmentioning

confidence: 99%

Predicting abundances of plants and pollinators using a simple compartmental mutualistic model

Fort

Mungan

2015

Proc. R. Soc. B.

View full text Add to dashboard Cite

Key gaps to be filled in population and community ecology are predicting the strength of species interactions and linking pattern with process to understand species coexistence and their relative abundances. In the case of mutualistic webs, like plant -pollinator networks, advances in understanding species abundances are currently limited, mainly owing to the lack of methodological tools to deal with the intrinsic complexity of mutualisms. Here, we propose an aggregation method leading to a simple compartmental mutualistic population model that captures both qualitatively and quantitatively the size-segregated populations observed in a Mediterranean community of nectar-producing plant species and nectarsearching animal species. We analyse the issue of optimal aggregation level and its connection with the trade-off between realism and overparametrization. We show that aggregation of both plants and pollinators into five size classes or compartments leads to a robust model with only two tunable parameters. Moreover, if, in each compartment, (i) the interaction coefficients fulfil the condition of weak mutualism and (ii) the mutualism is facultative for at least one party of the compartment, then the interactions between different compartments are sufficient to guarantee global stability of the equilibrium population.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting abundances of plants and pollinators using a simple compartmental mutualistic model

Fort

Mungan

2015

Proc. R. Soc. B.

View full text Add to dashboard Cite

show abstract

“…Guess [8] takes a different approach, letting users write simple script-language programs. All these systems use only NL, except that Pajek can perform block modeling [9] and print (but not display) their adjacency matrices.…”

Section: Systems For Social Network Explorationmentioning

confidence: 99%

“…There is a long tradition of matrix block modeling in the social sciences [9]. As Bertin stated, when adequately reordered, a matrix can reveal both global and local structures in a network [15].…”

Section: Layout For Node-link and Matrix Representationsmentioning

confidence: 99%

MatLink: Enhanced Matrix Visualization for Analyzing Social Networks

Henry

Fekete

2007

Lecture Notes in Computer Science

131

109

View full text Add to dashboard Cite

Abstract. Visualizing social networks presents challeges for both nodelink and adjacency matrix representations. Social networks are locally dense, which makes node-link displays unreadable. Yet, main analysis tasks require following paths, which is difficult on matrices. This article presents MatLink, a hybrid representation with links overlaid on the borders of a matrix and dynamic topological feedback as the pointer moves. We evaluated MatLink by an experiment comparing its readability, in term of errors and time, for social network-related tasks to the other conventional representations on graphs varying in size (small and medium) and density. It showed significant advantages for most tasks, especially path-related ones where standard matrices are weak.

show abstract

“…Formal network analysis and the conceptual emergence of relational thinking in the social sciences (see Marsden &Lin, 1982, andBerkowitz, 1988, for instance) have led to new research designs and have yielded ground-breaking empirical discoveries that challenge established categorical reasoning. New theoretical perspectives, methodologies, and concepts have been developing within the framework of relational thought (Doreian, Batagelj, & Ferligoj, 2005;Kilduff & Tsai, 2003;Snijders & Steglich, in press-a;Wasserman & Faust, 1994). Some scholars push this relational thinking as far as arguing that the basic assumption of a relational social science is the "anticategorical imperative" (Emirbayer & Goodwin, 1994, p. 1414.…”

Section: Social Processes Social Network and Distancementioning

confidence: 99%

Exploring the Interaction of Space and Networks in the Creation of Knowledge: An Introduction

Glückler

Lazega

Hammer

2017

Knowledge and Space

View full text Add to dashboard Cite

The book series on Knowledge and Space explores the nature of human knowledge from a geographical perspective. How to create, share, and adopt new knowledge is a core question in the social sciences. Processes of learning and knowledge creation are the result of social practice and always take place in space and in specific geographical contexts. The eleventh volume is the outcome of the symposium entitled "Topographies and Topologies of Knowledge" in the series of Klaus Tschira Symposia on Knowledge and Space held at the Villa Bosch Studio in Heidelberg. This book focuses on the conceptual and empirical intersections of the geographical and network dimensions of social practice in accounting for the creation and reproduction of knowledge. By taking up this dialogue between the fields of geography and social network studies, the book is conceived to bridge a research gap between two analytic perspectives that until recently have developed more in parallel to each other than in mutual exchange between scholars. The intention of its chapters is to broaden and deepen understanding of the specific characteristics not only of space and connectivity but also of their mutual and interactive effect on knowledge creation.

show abstract

Generalized Blockmodeling

Cited by 297 publications

References 0 publications

Predicting abundances of plants and pollinators using a simple compartmental mutualistic model

Predicting abundances of plants and pollinators using a simple compartmental mutualistic model

MatLink: Enhanced Matrix Visualization for Analyzing Social Networks

Exploring the Interaction of Space and Networks in the Creation of Knowledge: An Introduction

Contact Info

Product

Resources

About